---
title: SGLang Diffusion
description: Accelerated image and video generation with diffusion models.
---
SGLang Diffusion is a high-performance inference framework for image and video generation. It provides native SGLang pipelines, diffusers backend support, an OpenAI-compatible server, and an optimized kernel stack built on both precompiled `sgl-kernel` operators and JIT kernels for key inference paths.

## Key Features

- Broad model support across Wan, Hunyuan, Qwen-Image, FLUX, Z-Image, GLM-Image, and more
- Fast inference with `sgl-kernel`, JIT kernels, scheduler improvements, and caching acceleration
- Multiple interfaces: `sglang generate`, `sglang serve`, and an OpenAI-compatible API
- Multi-platform support for NVIDIA, AMD, Intel XPU, Ascend, Apple Silicon, and Moore Threads

## Quick Start

```bash
uv pip install "sglang[diffusion]" --prerelease=allow
```

```bash
sglang generate --model-path Qwen/Qwen-Image \
  --prompt "A beautiful sunset over the mountains" \
  --save-output
```

```bash
sglang serve --model-path Qwen/Qwen-Image --port 30010
```

## Start Here

- [Installation](/docs/sglang-diffusion/installation): install SGLang Diffusion and platform dependencies
- [Supported Models and Optimization Compatibility](/docs/sglang-diffusion/compatibility_matrix): check supported model families, long-tail coverage, and optimization support
- [CLI](/docs/sglang-diffusion/api/cli): run one-off generation jobs or launch a persistent server
- [OpenAI-Compatible API](/docs/sglang-diffusion/api/openai_api): send image and video requests to the HTTP server
- [Performance Overview](/docs/sglang-diffusion/performance-optimization): choose speed, memory, parallelism, caching, and quality-tradeoff levers
- [Caching Acceleration](/docs/sglang-diffusion/caching-acceleration): use Cache-DiT, TeaCache, or Spectrum to reduce denoising cost
- [Quantization](/docs/sglang-diffusion/quantization): load quantized transformer checkpoints
- [Contributing](/docs/sglang-diffusion/contributing): contribution workflow, adding new models, and CI perf baselines

## Additional Documentation

- [Post-Processing](/docs/sglang-diffusion/api/post_processing): frame interpolation and upscaling
- [Models with AR Stage](/docs/sglang-diffusion/models_with_ar): run hybrid diffusion pipelines like GLM-Image with a separate AR encoder server
- [Models with Prompt Enhancement](/docs/sglang-diffusion/models_with_pe): run ERNIE-Image with either built-in PE or a separate PE server
- [Deployment and Performance Modes](/docs/sglang-diffusion/deployment_cookbook): choose `--performance-mode`, offload, FSDP, CFG parallelism, SP, and TP
- [Attention Backends](/docs/sglang-diffusion/attention_backends): choose the best backend for your model and hardware
- [Sequence Parallelism](/docs/sglang-diffusion/ring_sp_performance): configure SP, Ulysses, and ring-based splitting for long sequences
- [Encoder Parallelism](/docs/sglang-diffusion/encoder_parallel): fold, data-parallel, or replicate the text/image encoders across idle GPUs
- [Inference Batching](/docs/sglang-diffusion/dynamic_batching): batch compatible native diffusion requests during serving
- [Progressive Resolution Generation](/docs/sglang-diffusion/progressive_resolution): run early denoising steps at lower latent resolution for selected pipelines
- [Environment Variables](/docs/sglang-diffusion/environment_variables): platform, caching, storage, and debugging configuration

## Developer Documentation

- [Support New Models](/docs/sglang-diffusion/support_new_models): implementation guide for new diffusion pipelines
- [CI Performance Baselines](/docs/sglang-diffusion/ci_perf): generate and update performance baselines used in CI

## References

- [SGLang GitHub](https://github.com/sgl-project/sglang)
- [Cache-DiT](https://github.com/vipshop/cache-dit)
- [FastVideo](https://github.com/hao-ai-lab/FastVideo)
- [xDiT](https://github.com/xdit-project/xDiT)
- [Diffusers](https://github.com/huggingface/diffusers)
